Integrated genomics and metabolomics to identify cause-specific biomarkers for chronic kidney disease in a Korean...

The heterogeneity of chronic kidney disease (CKD) and fragmented analysis methods hinder the precise identification of novel biomarkers. We addressed this challenge using two independent cohorts to integrate genomics and metabolomics, aiming to identify cause-specific biomarkers for CKD in the Korean population.

A longitudinal genome-wide association study using the Cox proportional hazards model was conducted using the Ansan and Ansung cohort. To validate these genomic biomarkers and integrate them with plasma metabolomics biomarkers, we utilized a hospital-based biopsy cohort to identify cause-specific CKD biomarkers. Within the biopsy cohort, we analyzed four disease subsets, including type 2 diabetic kidney disease (DKD), hypertensive nephropathy (HN), immunoglobulin A nephropathy (IgAN), and membranous nephropathy (MN), and compared them with healthy individuals. Significant single nucleotide polymorphisms(SNPs) and metabolites for each CKD subset were identified through logistic regression and correlation-based network analyses. Subsequently, we analyzed the risk of disease progression associated with the identified pairs.

A total of 448 variants associated with CKD occurrence were identified, with significant differences in several genetic variants and metabolites observed among patients with DKD, HN, IgAN, and MN compared to healthy individuals. Among 36 SNP-metabolite pairs, those involing FOXB1 and ZFP42 were associated with DKD, whereas pairs involving MMRN1 and SYNJ2 were linked to MN. Notably, the rs1025170 variant in FOXB1 and tyrosine pair was correlated with DKD progression.

Integrating genomics and metabolomics across independent cohorts enables the discovery of cause-specific biomarkers for the occurrence and progression of CKD in the Korean population.